Performance measurement in supply chain using the scor model and an application in the automotive industry

In the context of global competition, the comprehensive measurement of supply chain performance using both financial and non-financial indicators is critically important for sustainable businesses. However, data uncertainty and integration challenges render traditional methods insufficient. This study presents a seven-step methodology aiming to be a pioneer in supply chain management by integrating commercial performance measurement. Specifically, a grey-based neighborhood rough clustering method, developed to handle data scarcity and uncertainty, was integrated with resilience-focused attributes such as reliability, responsiveness, and agility from the SCOR Model's metrics. This integrated approach was then applied to the supply chain activities of a multi-site (12 facilities) company operating in the automotive sector and focuses on metrics that are fundamental to supply chain performance among the many metrics in the SCOR Model. The processing of subjective data obtained via a Likert scale, alongside ERP data using grey system theory, and the inclusion of external performance indicators, demonstrated that this integrated approach overcomes complexities, providing reliable and flexible evaluations. The findings highlight the flexibility and parameter sensitivity of grey-based numbers. This study significantly contributes to the literature by combining grey system theory with uncertain data of the resilience function, offering a crucial approach for sustainable supply chain management.